Industrial and power generation turbines have control systems (“controllers”) that monitor and control their operation. These controllers govern the combustion system of the turbine and other operational aspects of the turbine. For illustrative purposes, a gas turbine is described herein; however, it is appreciated that the embodiments may also apply to other turbine types and are not limited to gas turbine modeling or control. The controller may execute scheduling algorithms that adjust the fuel flow, inlet guide vanes (“IGV”), and other control inputs to ensure safe and efficient operation of the turbine.
Typically, output (e.g., power production) of a turbine can vary significantly with changes in external factors that cannot be controlled. Example uncontrollable external factors include ambient conditions, such as temperature, humidity, pressure, etc. These factors can cause operating inefficiencies as the external factors change, making it difficult to predict and control turbine performance at a given operating condition prior to actually experiencing the changing behavior. In addition, machine degradation that is not accounted for can also increase the difficulties to predict and control turbine performance.
As one example, grid compliance and dispatch planning may be affected adversely by controlling a turbine in a relatively static manner, using static control profiles, such as heat rate curves gathered from only periodic performance tests of turbine operation. Between these periodic updates, turbine performance may change (e.g., from degradation), which may affect start-up and load performance and operating characteristics. Moreover, intraday changes in these external factors, without accounting for the same in the turbine control profiles, may in turn cause inefficient turbine operation. To compensate for these generally unaccounted for changes, turbine operation may be controlled in an overly conservative manner, not achieving the full utilization, output, and thus operating efficiency as possible.
Without identifying the short-term inefficiencies and/or long-term deterioration as it occurs, a conventional schedule-based turbine control system will either have to be re-tuned periodically, have operating boundaries set artificially low (or high as some boundaries might require) to preemptively accommodate component deterioration, or risk violating operational boundaries that may lead to excessive fatigue or failure. Similarly, conventional schedule-based turbine control systems may also not be able to effectively accommodate changing conditions (e.g., gas quality, ambient conditions, etc.) to either tune for the most efficient operation or to avoid violation of component operational limits.
Thus, there exists a need for methods and systems for modeling turbine operation.